Machine learning driven metal oxide-based portable sensor array for on-site detection and discrimination of...

Area of Science:

• Analytical Chemistry
• Materials Science
• Food Science

Background:

• Fungal contamination of cereals, grains, and feedstuffs is a significant concern.
• Mycotoxins produced by fungi pose risks to food quality, safety, and human health.
• Rapid, on-site detection methods are essential for effective food safety monitoring.

Purpose of the Study:

• To develop an efficient sensor array for on-site detection and segregation of mycotoxins in corn.
• To utilize hierarchically modified metal oxides with azodye-based metal complexes for mycotoxin sensing.
• To ensure food safety and quality through reliable mycotoxin analysis.

Main Methods:

• Fabrication of a sensor array using hierarchically modified metal oxides and azodye-based metal complexes.
• Characterization of the functionalized material using advanced analytical techniques.
• Detection and differentiation of five mycotoxins using the sensor array and linear discriminant analysis (LDA).

Main Results:

• The sensor array achieved 100% efficiency in detecting and differentiating five distinct mycotoxins.
• Linear discriminant analysis (LDA) score plots validated the array's performance.
• The limit of detection ranged from 0.02 to 0.09 ppm for the mycotoxins.
• The sensor array demonstrated 100% accuracy in discriminating binary and ternary mycotoxin ratios in real samples.

Conclusions:

• The developed sensor array offers a highly efficient and accurate solution for on-site mycotoxin detection in corn.
• This technology is crucial for ensuring the safety and quality of cereals and feedstuffs.
• The sensor array provides a reliable tool for rapid food safety monitoring and segregation.